Final specimen mapping#
Kasumi1 naive#
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import pandas as pd
import sys
sys.path.append('../')
from source.bokeh_plots import *
from source.data_visualization import *
output_notebook()
mount = '/mnt/e/'
input_path = mount + 'MethylScore_v2/Processed_Data/'
test_sample_name = 'kasumi1_naive'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')
plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
xaxis = "PaCMAP 1 of 2", yaxis = "PaCMAP 2 of 2",
cols=['WHO 2022 Diagnosis'])
df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
Note
Though we generate the PaCMAP embedding in 2 dimensions (x and y axis) for visualization purposes, the predictive models were trained using 5 dimensions, so the following plots show the first two dimensions out of 5.
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plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
xaxis = "PaCMAP 1 of 5", yaxis = "PaCMAP 2 of 5",
x_range=(-25, 25), y_range=(-25, 25),
cols=['WHO 2022 Diagnosis'])
df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
Show code cell output
| AML Epigenomic Risk | AML Epigenomic Risk P(High Risk) | AL Epigenomic Phenotype | P(AML with ETV6 fusion) | |
|---|---|---|---|---|
| kasumi1_naive | Low | 0.249 | AML with ETV6 fusion | 0.952 |
Kasumi1 + 0.7uM Decitabine harvested 72h#
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test_sample_name = 'kasumi1_07deci'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')
plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
xaxis = "PaCMAP 1 of 5", yaxis = "PaCMAP 2 of 5",
x_range=(-25, 25), y_range=(-25, 25),
cols=['WHO 2022 Diagnosis'])
df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
Show code cell output
| AML Epigenomic Risk | AML Epigenomic Risk P(High Risk) | AL Epigenomic Phenotype | P(AML with mutated NPM1) | |
|---|---|---|---|---|
| kasumi1_07deci | High | 0.753 | AML with mutated NPM1 | 0.408 |
The confidence of the model droppped significantly with hypomethylating agent exposure (40.8% confidence).
UF HemBank 1829#
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test_sample_name = 'uf_hembank_1829'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')
plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
xaxis = "PaCMAP 1 of 5", yaxis = "PaCMAP 2 of 5",
x_range=(-25, 25), y_range=(-25, 25),
cols=['WHO 2022 Diagnosis'])
df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
Show code cell output
| AML Epigenomic Risk | AML Epigenomic Risk P(High Risk) | AL Epigenomic Phenotype | P(AML with t(v;11q23); KMT2A-r) | |
|---|---|---|---|---|
| uf_hembank_1829 | High | 0.724 | AML with t(v;11q23); KMT2A-r | 0.976 |
UF HemBank 1830#
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test_sample_name = 'uf_hembank_1830'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')
plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
xaxis = "PaCMAP 1 of 5", yaxis = "PaCMAP 2 of 5",
x_range=(-25, 25), y_range=(-25, 25),
cols=['WHO 2022 Diagnosis'])
df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
Show code cell output
| AML Epigenomic Risk | AML Epigenomic Risk P(High Risk) | AL Epigenomic Phenotype | P(AML with t(v;11q23); KMT2A-r) | |
|---|---|---|---|---|
| uf_hembank_1830 | High | 0.881 | AML with t(v;11q23); KMT2A-r | 0.974 |
UF HemBank 1831#
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test_sample_name = 'uf_hembank_1831'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')
plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
xaxis = "PaCMAP 1 of 5", yaxis = "PaCMAP 2 of 5",
x_range=(-25, 25), y_range=(-25, 25),
cols=['WHO 2022 Diagnosis'])
df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
Show code cell output
| AML Epigenomic Risk | AML Epigenomic Risk P(High Risk) | AL Epigenomic Phenotype | P(Otherwise-Normal Control) | |
|---|---|---|---|---|
| uf_hembank_1831 | Low | 0.354 | Otherwise-Normal Control | 0.991 |
UF HemBank 1832#
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test_sample_name = 'uf_hembank_1832'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')
plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
xaxis = "PaCMAP 1 of 5", yaxis = "PaCMAP 2 of 5",
x_range=(-25, 25), y_range=(-25, 25),
cols=['WHO 2022 Diagnosis'])
df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
Show code cell output
| AML Epigenomic Risk | AML Epigenomic Risk P(High Risk) | AL Epigenomic Phenotype | P(Otherwise-Normal Control) | |
|---|---|---|---|---|
| uf_hembank_1832 | Low | 0.149 | Otherwise-Normal Control | 0.991 |
UF HemBank 1841#
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test_sample_name = 'uf_hembank_1841'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')
plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
xaxis = "PaCMAP 1 of 5", yaxis = "PaCMAP 2 of 5",
x_range=(-25, 25), y_range=(-25, 25),
cols=['WHO 2022 Diagnosis'])
df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
Show code cell output
| AML Epigenomic Risk | AML Epigenomic Risk P(High Risk) | AL Epigenomic Phenotype | P(Otherwise-Normal Control) | |
|---|---|---|---|---|
| uf_hembank_1841 | High | 0.678 | Otherwise-Normal Control | 0.988 |
UF HemBank 1852#
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test_sample_name = 'uf_hembank_1852'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')
plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
xaxis = "PaCMAP 1 of 5", yaxis = "PaCMAP 2 of 5",
x_range=(-25, 25), y_range=(-25, 25),
cols=['WHO 2022 Diagnosis'])
df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
Show code cell output
| AML Epigenomic Risk | AML Epigenomic Risk P(High Risk) | AL Epigenomic Phenotype | P(AML with NUP98-fusion) | |
|---|---|---|---|---|
| uf_hembank_1852 | High | 0.786 | AML with NUP98-fusion | 0.83 |